1. Field of the Invention
The present invention pertains to seismic surveying and, more particularly, to a technique for use in analyzing the resultant information.
2. Description of the Related Art
Seismic exploration is conducted both on land and in water. In both environments, exploration involves surveying subterranean geological formations for hydrocarbon deposits. A survey typically involves deploying acoustic source(s) and acoustic sensors at predetermined locations. The sources impart acoustic waves into the geological formations. Features of the geological formation reflect the acoustic waves to the sensors. The sensors receive the reflected waves, which are detected, conditioned, and processed to generate seismic data. Analysis of the seismic data can then indicate probable locations of the hydrocarbon deposits.
One technique for analyzing the seismic data is called amplitude variation with offset (“AVO”). AVO is a variation in seismic reflection amplitude with change in distance between a source and a receiver that indicates differences in lithology and fluid content in rocks above and below the reflector. AVO analysis is a technique by which geophysicists attempt to determine characteristics of the geological formation such as thickness, porosity, density, velocity, lithology and fluid content of rocks. Successful AVO analysis employs certain well-known techniques for processing seismic data and seismic modeling of the seismic data to determine rock properties with a known fluid content. With that knowledge, it is possible to model other types of fluid content.
Seismic modeling is the comparison, simulation or representation of seismic data to define the limits of seismic resolution, assess the ambiguity of interpretation or make predictions. Generation of a synthetic, or modeled, seismogram from a well log and comparing the synthetic, or modeled trace, with seismic data is a common direct modeling procedure. Generating a set of pseudologs, or synthetic data, from seismic data is the process known as seismic inversion, a type of indirect modeling. Models can be developed to address problems of structure and stratigraphy prior to acquisition of seismic data and during the interpretation of the data. One type of inversion is pre-stack waveform inversion (“PSWI”).
The interest shown by exploration seismologists in amplitude-variation-with-offset (“AVO”) analysis for the direct detection of hydrocarbons from seismic data has been growing over the past few years. Reflection records of prestack seismic data contain valuable amplitude information that can be related to the subsurface lithology. With the increasing popularity of AVO, considerable work has also been carried out on the AVO inversion, and the fundamental problem of non-uniqueness associated with such an inversion is now well recognized. See, e.g., Hampson, D., “AVO Inversion, Theory, and Practice”, 10 The Leading Edge, 39-42 (1991); Sen, M. K., and Stoffa, P. L., “Genetic Inversion of AVO”, 11 The Leading Edge,”27-29 (1992).
Thus, both AVO and PSWI have been used for a number of years. Walden, AT., “Making AVO Sections More Robust”, 39 Geophysical Prospecting, 915-942 (1991), described a method for angle decomposition using a zero dip assumption as may be used in AVO. Mallick, S., “Model-Based Inversion of Amplitude-Variation-With-Offset Data Using a Genetic Algorithm,” 60 Geophysics, 939-954 (1995) (“Subhashis”) presented a method for PSWI, also with the assumption of zero reflector dip.
The pre-stack waveform inversion method described by Subhashis assumes that the input common image gathers are migrated with the correct velocity, but does not describe a method for determining the correct velocity field. More precisely, the PSWI technique of Subhashis is a genetic algorithm which attempts to match observed pre-stack seismic data by forward modeling using a trial velocity field. This trial velocity field, which must be laterally invariant, is perturbed until the modeled data matches the observed data as closely as possible. The velocity field which yields the optimum match is the output from the inversion. The input data is normally a pre-stack time migrated (“PSTM”) gather, and the inversion is independent for each gather. The method is often referred to as a 1D (“one-dimensional”) inversion which is not strictly true since the input to the process is 3D PST1M gathers. A more accurate term for the inversion would be zero dip, since the forward modeling uses zero for the reflector dip.
The present invention is directed to resolving, or at least reducing, one or all of the problems mentioned above.